实现多类高保真物体SLAM，实时运行且无需牺牲精度。
DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild
- 异步映射+传感器融合，支持鱼眼相机与激光雷达组合。
- 物体处理延迟降低70%，在25Hz数据集上实现实时性能。
- 适合自动驾驶、机器人操作等真实场景部署。
现有物体感知SLAM系统在实时性、多类别支持与生成高保真语义一致物体模型之间存在权衡。为此，本文提出DSP-SLAM++，在原有框架基础上引入异步映射管道以提升实时性，并针对单目鱼眼-激光雷达传感器套件设计专用融合策略。实验表明，该系统能生成多类物体的精细完整几何形状，同时通过将最大物体处理延迟降低高达70%的方式消除严重映射线程瓶颈，在25 Hz的复杂多类数据集上实现稳健实时性能。本工作使高保真多类物体SLAM更适用于真实应用，如自动驾驶与机器人操作，支持常见鱼眼-激光雷达平台部署。开源代码已发布于：[github.com/AUBVRL/DSP-SLAMpp]。
原文摘要 · Abstract (English)
Existing object-aware SLAM systems force a trade-off between real-time performance, multi-class support, and the generation of high-fidelity, semantically coherent object models. To address this trade-off, we present DSP-SLAM++, which extends the DSP-SLAM framework with an asynchronous mapping pipeline for real-time performance and dedicated sensor fusion adaptations for a monocular fisheye-LiDAR suite. Experiments demonstrate that our system generates fine-grained, geometrically-complete shapes for multiple object classes while eliminating severe mapping thread bottlenecks by reducing maximum object processing latency by up to 70\% compared to the state-of-the-art baseline, enabling robust, real-time performance on a challenging 25 Hz multi-class datasets. This work makes high-fidelity, multi-class object SLAM more practical for real-world applications like autonomous driving and robotic manipulation by enabling its use on platforms with common fisheye-LiDAR sensor setups. The open-source code is available at: [github.com/AUBVRL/DSP-SLAMpp].
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